Prescription Rate Data (Tamer)

Overdose Rate Data (Chris)

Prescription Rate vs. Overdose Rate (Chris)

1. Show maps
2. Ask Questions about whether one causes the other, etc.

Regression

# scatter / regression between prescription rate and overdose rate
mod <- lm(age_adjusted_rate ~ prescription_rate, data = full_data)
msummary(mod)

##                   Estimate Std. Error t value Pr(>|t|)   
## (Intercept)        7.56323    2.96818   2.548  0.01409 * 
## prescription_rate  0.10475    0.03524   2.972  0.00461 **
## 
## Residual standard error: 5.298 on 48 degrees of freedom
## Multiple R-squared:  0.1554, Adjusted R-squared:  0.1379 
## F-statistic: 8.835 on 1 and 48 DF,  p-value: 0.004609

ggplot(data = full_data, aes(x = prescription_rate)) +
  geom_histogram(bins = 15)

ggplot(data = full_data, aes(x = prescription_rate, y = age_adjusted_rate)) +
  geom_point() + 
  geom_abline(intercept = 7.56323, slope = 0.10475)

Determining the Optimal K

Explain silhouette score

silhouette_score <- function(k){
  km <- kmeans(full_data[, 2:3], centers = k, nstart = 20)
  score <- cluster::silhouette(km$cluster, dist(full_data[, 2:3]))
  mean(score[, 3])
}

k <- 2:5
avg_sil <- sapply(k, silhouette_score)
optimal_k <- which(as.data.frame(avg_sil)$avg_sil == max(avg_sil)) + 1
optimal_k

## [1] 2

km <- kmeans(full_data[, 2:3], centers = optimal_k, nstart = 20)
full_data <- mutate(full_data, cluster = as.character(km$cluster))

Clustering for 2014